SuperMag: Vision-based Tactile Data Guided High-resolution Tactile Shape Reconstruction for Magnetic Tactile Sensors

Peiyao Hou, Danning Sun, Meng Wang, Yuzhe Huang, Zeyu Zhang, Hangxin Liu, Wanlin Li, Ziyuan Jiao

Keywords: Magnetic-based tactile sensor, Vision-based tactile sensor, Tactile data interpretation

TL;DR: This paper proposes SuperMag, a tactile shape reconstruction method that addresses this limitation by leveraging high-resolution vision-based tactile sensor (VBTS) data to supervise magnetic-based tactile sensor (MBTS) super-resolution.

Abstract:

Abstract— Magnetic-based tactile sensors (MBTS) combine
the advantages of compact design and high-frequency operation but suffer from limited spatial resolution due to their
sparse taxel arrays. This paper proposes SuperMag, a tactile
shape reconstruction method that addresses this limitation by
leveraging high-resolution vision-based tactile sensor (VBTS)
data to supervise MBTS super-resolution. Co-designed, open-
source VBTS and MBTS with identical contact modules enable synchronized data collection of high-resolution shapes
and magnetic signals via a symmetric calibration setup. We
frame tactile shape reconstruction as a conditional generative
problem, employing a conditional variational auto-encoder to
infer high-resolution shapes from low-resolution MBTS inputs.
The MBTS achieves a sampling frequency of 125 Hz, whereas
the shape reconstruction sustains an inference time within
2.5 ms. This cross-modality synergy advances tactile perception
of the MBTS, potentially unlocking its new capabilities in high-
precision robotic tasks.

Submission Number: 13